Reinforcement learning (RL) is a promising direction towards allowing robots to acquire new skills through real-world interaction. Despite impressive results in simulated applications, e.g., simulated robots [1]}, the application of RL on physical robots is limited primarily due to the data-inefficiency of these algorithms [2]}, [3]}.
